Abstract

Open source tools have recently reached a level of maturity which
makes them suitable for building large-scale real-world systems. At
the same time, the field of machine learning has developed a large
body of powerful learning algorithms for diverse applications.
However, the true potential of these methods is not used, since
existing implementations are not openly shared, resulting in software
with low usability, and weak interoperability. We argue that this
situation can be significantly improved by increasing incentives for
researchers to publish their software under an open source
model. Additionally, we outline the problems authors are faced with
when trying to publish algorithmic implementations of machine learning
methods. We believe that a resource of peer reviewed software
accompanied by short articles would be highly valuable to both the
machine learning and the general scientific community.